What could or should make Oracle/MySQL antitrust concerns go away?
When the Oracle/MySQL deal was first announced, I wrote:
I can probably come up with business practices that could make things very hard on Oracle/MySQL competitors … but I haven’t found a compelling antitrust trigger on my first pass over the subject.
Subsequently, there’s been a lot of discussion about whether or not Oracle can use control of MySQL to make life difficult for third-party MySQL storage engine vendors.
Now that the European Commission is delaying the Oracle/Sun deal, explicitly because of Oracle/MySQL antitrust fears. That is, the European Commission wants to be reassured that an Oracle takeover of MySQL won’t unduly impinge upon the future availability of open source/low cost DBMS alternatives. This raises that natural question:
What could Oracle do to assure concerned parties that its ownership of MySQL won’t unduly hamper open-source-based DBMS competition?
I think that’s indeed the crucial question. The Oracle/Sun deal has enough momentum at this point that it both should and will be allowed to happen — perhaps with safeguards — rather than banned outright. If you have concerns about Oracle’s pending acquisition of MySQL, you should speak up and outline what kinds of regulatory safeguards would alleviate the problems you foresee.
More or less obvious possibilities include:
- Divest MySQL. This is obviously an extreme measure, but it surely would work.
- Provide some money and trademark rights to MySQL forkers. If MariaDB and Drizzle were put into strong competitive positions with MySQL today, it’s hard to argue how regulators could object to any future Oracle maneuverings Oracle might envision with the GPLed side of MySQL.
- Offer a standard, attractive, long-term deal to MySQL bundlers. The commercial/non-GPL version of MySQL is a requirement for appliance vendors (surely), OEM vendors (probably), and storage engine vendors (maybe — I disagree, but I’m evidently in the minority).
- Strengthen PostgreSQL. 🙂 Realistically, that’s not going to be part of any Oracle/MySQL resolution, so I’ll leave it as a subject for another time.
| Categories: Mid-range, MySQL, Open source, Oracle, PostgreSQL | 9 Comments |
Teradata really means that those 100+ appliances are in PRODUCTION
I was misremembering. It turns out that when Teradata said it had over 100 appliances “in production”, it meant that >100 hardware-based appliances are actually in production. If you add in the software-only “appliances,” and count test/development as well as true production, the total rises to >200.
I tried to get a finer breakdown out of Teradata on a disclosable basis, but failed. The ostensible reason is that public companies often don’t do that sort of thing without permission from the investor relations department, and Teradata’s marketers evidently haven’t felt a sense of urgency about getting permission to, for example, communicate how well just the 25xx series is doing.
| Categories: Data warehouse appliances, Data warehousing, Market share and customer counts, Teradata | 1 Comment |
Continuent on clustering
Robert Hodges, CTO of my client Continuent, put up a blog post laying out his and Continuent’s views on database clustering. Continuent offers Tungsten, its third try at database clustering technology, targeted at MySQL, PostgreSQL, and perhaps Oracle. Unlike Continuent’s more ambitious. second-generation product, Tungsten offers single-master replication, which in Robert’s view allows for great ease of deployment and administration (he likes the phrase “bone-simple”).
The downside to Continuent Tungsten ‘s stripped down architecture is that it doesn’t solve the most extreme performance scale-out problems. Instead, Continuent focuses on the other big benefits of keeping your data in more than one place, namely high availability and data loss prevention (i.e., backup).
Continuent has been around for a number of years, starting out in Finland but now being based in Silicon Valley. For most purposes, however, it’s reasonable to think of Continuent and Tungsten as start-up efforts.
As you might guess from the references to Finland and MySQL, Continuent’s products are open source, or at least have open source versions. I’m still a little fuzzy as to which features are open sourced and which are not. For that matter, I’m still unclear as to Tungsten’s feature list overall …
| Categories: Clustering, Continuent, MySQL, Open source, PostgreSQL | 3 Comments |
Teradata and Netezza are doing MapReduce too
Netezza told me a while ago that it planned to introduce MapReduce, and agreed yesterday this was no longer NDAed. Stephen Brobst of Teradata* let slip at XLDB that Teradata has MapReduce too, apparently implemented but not yet generally available.
I don’t have details in either case. Netezza and Teradata evidently aren’t taking MapReduce as seriously as Aster Data, or even Greenplum or Vertica. But MapReduce has become pretty much of a “checkmark” item for large-database analytic DBMS vendors even so.
*Technically, Brobst is not and never has been a Teradata employee — but he’s widely and correctly regarded as being “of Teradata” even so. 🙂
| Categories: Data warehousing, MapReduce, Netezza, Teradata | 6 Comments |
SAS on Netezza and other Netezza extensibility
I chatted with SAS CTO Keith Collins yesterday about the new SAS/Netezza in-database parallel data mining scoring offering. My impression is that this is very similar to SAS’ current Teradata support, notwithstanding SAS’ and Teradata’s apparent original intention of offering in-database modeling by now as well.
I gather this is a big performance-enhancing deal, just as it is for SPSS or Oracle’s own data mining over Oracle. However, I must confess to not yet understanding why. That is, I don’t know what’s so complicated about data mining scoring algorithms that makes hand-coding them in SQL particularly forbidding. My naive view of data mining is that you do a big regression to get a bunch of weights, and the resulting scoring algorithm is a linear combination of a few dozen variables. Evidently, that’s not quite right.
Anyhow, it turns out that SAS held off on this work until it could be done for TwinFin. That’s largely because TwinFin lets partners write code on Intel CPUs, while previously they had to write in C for Netezza’s FPGAs. I got a similar sense from at least one other Netezza partner as well.
| Categories: Data warehouse appliances, Data warehousing, Netezza, Predictive modeling and advanced analytics, SAS Institute | 5 Comments |
Oracle Exadata hybrid columnar compression
Oracle Database 11g Release 2 is out, and as usual I wasn’t briefed — perhaps because Oracle is more scared than its competitors are of hard questions, perhaps for some other reason entirely.* Anyhow, Oracle Database 11 Release 2 contains an Exadata-only feature called hybrid columnar compression. The Oracle Database 11g Release 2 white paper says “data is grouped, ordered, and stored one column at a time.” But Kevin Closson clarifies:
The word hybrid is important.
Rows are still used. They are stored in an object called a Compression Unit. Compression Units can span multiple blocks. Like values are stored in the compression unit with metadata that maps back to the rows.
So, “hybrid” is the word. But, none of that matters as much as the effectiveness. This form of compression is extremely effective.
That sounds a whole lot like PAX. Specifically, in Oracle’s case I would guess “hybrid columnar compression” provides the compression benefits of column stores, but not column stores’ I/O benefits, and also not any kind of in-memory compression. Read more
| Categories: Columnar database management, Data warehousing, Database compression, Exadata, Oracle, Theory and architecture | 21 Comments |
Teradata has over 100 appliances in production
I recently wrote that Teradata had gotten serious about appliance product lines, and had non-trivial sales figures for them. In a press release today, Teradata is now explicitly saying (emphasis mine):
Teradata now has more than 100 appliances in production, including the Data Mart Appliance 551, the Data Warehouse Appliance 2550, and the Extreme Data Appliance 1550, which complement the core platform, the Teradata Active Enterprise Data Warehouse 5550.
The breakdowns on that are NDA, and anyhow I can’t find them immediately in my notes.* But if memory serves — while a lot of those appliances are used for test and development, a whole other lot of them are used to do actual production query-answering work. (Edit: Memory turned out to be wrong.) Read more
| Categories: Data warehouse appliances, Data warehousing, Market share and customer counts, Teradata | 2 Comments |
Sybase IQ technical highlights
General highlights of the Sybase IQ technical story include:
- Sybase IQ is an analytic DBMS with a columnar/column-store architecture
- Unlike most analytic DBMS, Sybase IQ has a shared-disk architecture.
- The Sybase IQ indexing story is a bit complicated, with a bunch of different index kinds. Most are focused on columns with low cardinality, and it least in some cases are a lot like bitmaps. (Sybase IQ when first introduced was a pure bitmap index product, with a single index type “Fast Project”.) But one index kind, “High Group” — designed for columns with high cardinality – is an exception to most generalities about other Sybase IQ index kinds, and instead is more akin to a b-tree.
- Unlike Vertica, Sybase stores each column of data only once. I don’t see how it would make sense to have multiple indexes on the same column, but I didn’t actually ask whether doing so is possible or common.
- Sybase estimates that Sybase IQ requires ¼ the DBA effort of, say, Oracle. (Frankly, that’s not a particularly good figure.) Obviously, this is just a broad-brush average.
- Sybase recently repurposed an acquired ETL tool to be focused on Sybase IQ. IQ of course also works with various third-party tools, certified or otherwise.
- Sybase’s Power Designer CASE (Computer-Aided Software Engineering)/database design tool works with Sybase IQ.
- Sybase is proud of Sybase IQ’s new in-database analytics capabilities, but I haven’t yet grasped what, if anything, is differentiated about them.
- Sybase has an ILM (Information Lifecycle Management) story built around the point that different columns can be stored on different kinds of media.
Highlights of the Sybase IQ compression story include: Read more
| Categories: Analytic technologies, Columnar database management, Data warehousing, Database compression, EAI, EII, ETL, ELT, ETLT, Sybase, Theory and architecture | 11 Comments |
Sybase IQ business notes
As specialized analytic DBMS go, Sybase is near the top of the charts both in age (Sybase IQ was first introduced in the mid 1990s) and adoption. That’s even more true, of course, if we restrict the discussion strictly to columnar DBMS, aka column stores. Basic Sybase IQ adoption claims include:
- >1500 users
- >3000 installations (Sybase has variously cited 2.1 and 2.5+ as the installation/user ratio)
- At least ~50-60 installations with >5 terabytes of user data
Note that 98% of Sybase IQ installations are under 5 terabytes; the heart of Sybase IQ’s business is the sub-terabyte data warehouse market.* Read more
| Categories: Analytic technologies, Data mart outsourcing, Data warehousing, Investment research and trading, Sybase | 3 Comments |
Teradata highlights some analytic use cases
A couple of slides in my recent briefing on Teradata’s Active Enterprise Data Warehouse Story contained long lists of analytic use cases, at a finer level of granualarity than I’m focusing on for a September speaking tour. I think they’re interesting to pass along. Read more
| Categories: Analytic technologies, Data warehousing, Teradata | 2 Comments |